Lake Dataset Research Articles

Eutrophication state in the Eastern China based on Landsat 35-year observations

Eutrophication of the eastern plain lake (EPL) region has a significant impact on the sustainable economic development and is closely related to the shortage of water resources in China. Remote sensing provides an effective tool for quantifying the trophic state of inland waters by associating the trophic state index (TSI) with optically active water quality parameters. However, limited by the satellite coverage range and operation time, the long-term changes in the trophic state of the EPL region have not been thoroughly investigated. This study aims to fill this gap by generating a 35-year (1986–2020) TSI dataset of lakes in the eastern plain based on Landsat images. The TSI inversion algorithm based on the algal biomass index (ABI) was designed for Landsat series after consistency analysis. The seasonal variations of the TSI showed the highest TSI (62.0 ± 11.4) in summer and the lowest TSI (51.6 ± 8.0) in winter, with uncertainties caused by the limitation of ABI for extremely turbid waters and the number of Landsat seasonal images. The TSI of the EPLs increased over the past 35 years by about 8.2%. Four change patterns were defined for the long-term interannual TSI variations: increasing trend less than 50% (Mode 1) or more than 50% (Mode 2), breaking points that show a surge trend (Mode 3), and decreasing trend (Mode 4). The contribution of meteorological and anthropogenic factors was calculated using a generalized linear model, which revealed that the eutrophication of inland lakes in the EPL region is mainly affected by industrial wastewater discharge and urban expansion. The influence of these explanatory variables becomes more complex with an increase in lake area. Our research provides an estimation of the TSI for the first 35-year basin-scale in the EPL region and a comprehensive evaluation of the driving factors of inland water eutrophication. The results can be used for the effective management and restoration of lakes.

Monthly mean remote sensing water transparency dataset of large lakes in China during 2000—2020

Monthly mean remote sensing water transparency dataset of large lakes in China during 2000—2020

Glacial Lake Area Changes in High Mountain Asia during 1990-2020 Using Satellite Remote Sensing.

Changes in a large-scale glacial lake area directly reflect the regional glacier status and climate changes. However, long time series of glacial lake dataset and comprehensive investigation of the spatiotemporal changes in the glacial lake area in the whole High Mountain Asia (HMA) region remained elusive. Satellite remote sensing provides an indispensable way for dynamic monitoring of glacial lakes over large regions. But glacial lakes are quite small and discretely distributed, and the extraction of glacial lakes is usually influenced by clouds, snow/ice cover, and terrain shadows; thus, there is a lack of an automatic method to continuously monitor the dynamic changes of glacial lakes in a large scale. In this paper, we developed a per-pixel composited method named the "multitemporal mean NDWI composite" to automatically extract the glacial lake area in HMA from 1990 to 2020 using time-series Landsat data. There were 19,294 glacial lakes covering a total area of 1471.85 ± 366.42 km2 in 1990, and 22,646 glacial lakes with an area of 1729.08 ± 461.31 km2 in 2020. It is noted that the glacial lake area in the whole HMA region expanded by 0.58 ± 0.21%/a over the past three decades, with high spatiotemporal heterogeneity. The glacial lake area increased at a consistent speed over time. The fastest expansion was in East Kun Lun at an average rate of 2.01 ± 0.54%/a, while in the Pamir and Hengduan Shan, they show slow increases with rates of 0.33 ± 0.08%/a and 0.39 ± 0.01%/a, respectively, during 1990-2020. The greatest increase in lake area occurred at 5000-5200 m a.s.l., which increased by about 45 km2 (~25%). We conclude that the temperature rise and glacier thinning are the leading factors of glacial lake expansion in HMA, and precipitation is the main source of lake water increase in West Kun Lun. Using the proposed method, a large amount of Landsat images from successive years of melting seasons can be fully utilized to obtain a pixel-level composited cloud-free and solid snow/ice-free glacial lake map. The uncertainties from supraglacial ponds and glacial meltwater were also estimated to improve the reliability and comparability of glacial lake area changes among different regions. This study provides important technical and data support for regional climate changes, glacier hydrology, and disaster analysis.

GAN-GL: Generative Adversarial Networks for Glacial Lake Mapping

Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways—random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts—a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global–local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention.

The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

ESTIMATING THE UNCERTAINTY OF KRIGING ESTIMATES: A PRACTICAL REVIEW AND THE PROPOSAL OF TWO NOVEL-APPROACHES

This paper presents two novel kriging-based methods to measure the uncertainty associated with geostatistical estimates. Both approaches combine the Kriging Variance (KV), which is a good summary of the spatial configuration of the data, with a second component which locally measures the data dispersion. The first proposed method, referred to as the Combined Index (CI), merges KV with the local conditional distribution estimated by median Indicator Kriging. The other method is named Combined Variance II (CVII) and it is an adaptation of the originally proposed Combined Variance, where the equation used to merge KV and the Interpolation Variance was replaced. The two proposed methods are compared to other approaches available in the literature in a case study using the widely known Walker Lake dataset. The results indicate that both proposed methods outperformed the other tested solutions. The CVII index outperformed its original formulation in all the tested scenarios.

High probability of nitrogen and phosphorus co-limitation occurring in eutrophic lakes

Limnologists and governments have long had an interest in whether nitrogen (N) and/or phosphorous (P) limit algal productivity in lakes. However, the types and importance of anthropogenic and biogeochemical processes of N and P differ with lake trophic status. Here, a global lake dataset (annual average data from 831 lakes) demonstrates that total nitrogen (TN): total phosphorous (TP) ratios declined significantly as lakes become more eutrophic. From oligotrophic to hypereutrophic lakes, the probability of N and P co-limitation significantly increases from 15.0 to 67.0%, while P-only limitation decreases from 77.0 to 22.3%. Furthermore, TN:TP ratios are mainly affected by concentrations of TP (r = −0.699) rather than TN (r = −0.147). These results reveal that lake eutrophication mainly occurs with increasing P rather than N, which shifts lake ecosystems from stoichiometric P limitation toward a higher probability of N and P co-limitation. This study suggests that low N:P stoichiometry and a high probability of N and P co-limitation tend to occur in eutrophic systems.

Recent Changes of Glacial Lakes in the High Mountain Asia and Its Potential Controlling Factors Analysis

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km2 and 1577.38 ± 288.82 km2; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region.

100 years of lake evolution over the Qinghai–Tibet Plateau

Abstract. Lakes can be effective indicators of climate change, and this is especially so for the lakes over the Qinghai–Tibet Plateau (QTP), the highest plateau in the world, which undergoes little direct human influence. The QTP has warmed at twice the mean global rate, and the lakes there respond rapidly to climate and cryosphere changes. The QTP has ∼ 1200 lakes larger than 1 km2 with a total area of ∼ 46 000 km2, accounting for approximately half the number and area of lakes in China. The lakes over the QTP have been selected as an essential example for global lakes or water body studies. However, concerning lake data over the QTP are limited to the Landsat era and/or available at sparse intervals. Here, we extend the record to provide comprehensive lake evolution data sets covering the past 100 years (from 1920 to 2020). Lake mapping in 1920 was derived from an early map of the Republic of China and in 1960 from the topographic map of China. The densest lake inventories produced so far between 1970 and 2020 (covering all lakes larger than 1 km2 in 14 epochs) are mapped from Landsat MSS, TM, ETM+, and OLI images. The lake evolution shows remarkable transitions between four phases: significant shrinkage in 1920–1995, rapid linear increase in 1995–2010, relative stability in 2010–2015, and further increase in 2015–2020. The spatial pattern indicates that the majority of lakes shrank in 1920–1995 and expanded in 1995–2020, with a dominant enlargement for central-north lakes in contrast to contraction for southern lakes in 1976–2020. The time series of precipitation between 1920 and 2017 indirectly supports the evolution trends of lakes identified in this study. The lake data set is freely available at https://doi.org/10.5281/zenodo.4678104 (Zhang et al., 2021a).

LAGOS‐US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.

AbstractMacroscale studies of aquatic ecosystems are needed to address many contemporary broad‐scale problems related to global change, particularly to inform national‐scale environmental policies. In this data paper, we fill two important gaps in data availability for lakes at the scale of the conterminous U.S., the lack of: (1) high‐resolution geographic representations of lakes and their watersheds and (2) an open data platform to connect disparate U.S. national‐scale lake datasets. We describe the LAGOS‐US LOCUS v1.0 data module that includes a detailed User Guide, which is part of the LAGOS‐US extensible research‐ready open data platform. This platform can be used to study the 479,950 lakes and reservoirs larger than or equal to 1 ha across the entire conterminous U.S. at multiple scales of space and time. The LOCUS module includes spatially‐explicit information on lakes and their watersheds, including location information, physical characteristics, and identifiers of other important national‐scale datasets.

Geographic variation and temporal trends in ice phenology in Norwegian lakes during the period 1890–2020

Abstract. Long-term observations of ice phenology in lakes are ideal for studying climatic variation in time and space. We used a large set of observations from 1890 to 2020 of the timing of freeze-up and break-up, and the length of ice-free season, for 101 Norwegian lakes to elucidate variation in ice phenology across time and space. The dataset of Norwegian lakes is unusual, covering considerable variation in elevation (4–1401 m a.s.l.) and climate (from oceanic to continental) within a substantial latitudinal and longitudinal gradient (58.2–69.9∘ N, 4.9–30.2∘ E). The average date of ice break-up occurred later in spring with increasing elevation, latitude and longitude. The average date of freeze-up and the length of the ice-free period decreased significantly with elevation and longitude. No correlation with distance from the ocean was detected, although the geographical gradients were related to regional climate due to adiabatic processes (elevation), radiation (latitude) and the degree of continentality (longitude). There was a significant lake surface area effect as small lakes froze up earlier due to less volume. There was also a significant trend that lakes were completely frozen over later in the autumn in recent years. After accounting for the effect of long-term trends in the large-scale North Atlantic Oscillation (NAO) index, a significant but weak trend over time for earlier ice break-up was detected. An analysis of different time periods revealed significant and accelerating trends for earlier break-up, later freeze-up and completely frozen lakes after 1991. Moreover, the trend for a longer ice-free period also accelerated during this period, although not significantly. An understanding of the relationship between ice phenology and geographical parameters is a prerequisite for predicting the potential future consequences of climate change on ice phenology. Changes in ice phenology will have consequences for the behaviour and life cycle dynamics of the aquatic biota.

An enhanced dual IDW method for high-quality geospatial interpolation

Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.

The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

AbstractThe intensity and frequency of storms are projected to increase in many regions of the world because of climate change. Storms can alter environmental conditions in many ecosystems. In lakes and reservoirs, storms can reduce epilimnetic temperatures from wind‐induced mixing with colder hypolimnetic waters, direct precipitation to the lake's surface, and watershed runoff. We analyzed 18 long‐term and high‐frequency lake datasets from 11 countries to assess the magnitude of wind‐ vs. rainstorm‐induced changes in epilimnetic temperature. We found small day‐to‐day epilimnetic temperature decreases in response to strong wind and heavy rain during stratified conditions. Day‐to‐day epilimnetic temperature decreased, on average, by 0.28°C during the strongest windstorms (storm mean daily wind speed among lakes: 6.7 ± 2.7 m s−1, 1 SD) and by 0.15°C after the heaviest rainstorms (storm mean daily rainfall: 21.3 ± 9.0 mm). The largest decreases in epilimnetic temperature were observed ≥2 d after sustained strong wind or heavy rain (top 5th percentile of wind and rain events for each lake) in shallow and medium‐depth lakes. The smallest decreases occurred in deep lakes. Epilimnetic temperature change from windstorms, but not rainstorms, was negatively correlated with maximum lake depth. However, even the largest storm‐induced mean epilimnetic temperature decreases were typically <2°C. Day‐to‐day temperature change, in the absence of storms, often exceeded storm‐induced temperature changes. Because storm‐induced temperature changes to lake surface waters were minimal, changes in other limnological variables (e.g., nutrient concentrations or light) from storms may have larger impacts on biological communities than temperature changes.

Linking physics and spatial statistics: A new family of Boltzmann-Gibbs random fields

We investigate a connection between spatial statistics and statistical physics to obtain new covariance functions with direct physical interpretation for spatial random fields. These covariance functions are based on the exponential Boltzmann-Gibbs representation and use an energy functional to represent interactions between the values of the random field at different points in space. This formulation results in closed-form generalized covariance functions, which display infinite variance in Euclidean spaces of dimension larger than one. We propose regularization schemes in real and reciprocal (spectral) space that lead to well-behaved covariance structures. The real-space regularization parameter allows a continuous interpolation between the Boltzmann-Gibbs covariance and the exponential covariance. We also propose discretized approximations on regular grids, and we show that they represent reparametrized versions of the well-known Besag and Leroux lattice models. We then discuss parameter estimation and spatial prediction for the regularized Boltzmann-Gibbs covariance model in two dimensions. We recommend using the pairwise difference likelihood that combines satisfactory estimation performance and good scalability with many observation points. The predictive performance of the regularized covariance function is assessed by means of cross-validation statistics. Irregularly-spaced samples from the Walker Lake dataset are used, and spatial prediction is conducted by means of ordinary kriging. The regularized Boltzmann-Gibbs covariance yields improved predictive performance compared to the exponential covariance model.

Methane emission from high latitude lakes: methane-centric lake classification and satellite-driven annual cycle of emissions

Methane (CH4) is emitted from lakes by several processes: bubbles released from bottom sediments that reach the atmosphere (ebullition); spring release of CH4 trapped in bubbles in and under the ice during fall freeze (bubble release), and diffusion of CH4 from sediments to the surface. Each of these emission routes is highly variable over space and time, and episodic in the extreme, making reliable measurements difficult to carry out. However, lakes are receiving increasing interest for their important contribution to global CH4 emissions. Their area, distribution and emissions respond to interannual and longer-term climate fluctuations and close to half the world’s lake area is in high northern latitudes that are experiencing rapidly-warming temperatures and lengthening thaw periods. We report on a new spatially-explicit data set of lakes > 50°N, classified with methane-relevant criteria. The seasonality of daily CH4 fluxes is driven with satellite observations of thaw timing and duration. We found that observed thaw seasons are 10–30% shorter than those assumed in previous studies. The area of lakes is 1,095 × 103 km2 and total CH4 emission is 13.8–17.7 Tg CH4 year−1: 11.2–14.4 Tg via diffusion and ebullition and 2.6–3.3 Tg from spring release of CH4 stored in bubbles in winter lake ice. This novel suite of data and methodologies provides a unique framework to model CH4 emission from lakes under current, past and future climates.

Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).

Water Depth Underpins the Relative Roles and Fates of Nitrogen and Phosphorus in Lakes.

Eutrophication mitigation is an ongoing priority for aquatic ecosystems. However, the current eutrophication control strategies (phosphorus (P) and/or nitrogen (N)) are guided mainly by nutrient addition experiments in small waters without encompassing all in-lake biogeochemical processes that are associated largely with lake morphological characteristics. Here, we use a global lake data set (573 lakes) to show that the relative roles of N vs P in affecting eutrophication are underpinned by water depth. Mean depth and maximum depth relative to mixing depth were used to distinguish shallow (mixing depth > maximum depth), deep (mixing depth < mean depth), and transitional (mean depth ≤ mixing depth ≤ maximum depth) lakes in this study. TN/TP ratio (by mass) was used as an indicator of potential lake nutrient limitation, i.e., N only limitation if N/P < 9, N + P colimitation if 9 ≤ N/P < 22.6, and P only limitation if N/P ≥ 22.6. The results show that eutrophication is favored in shallow lakes, frequently (66.2%) with N limitation while P limitation predominated (94.4%) in most lakes but especially in deep ones. The importance of N limitation increases but P limitation decreases with lake trophic status while N and P colimitation occurs primarily (59.4%) in eutrophic lakes. These results demonstrate that phosphorus reduction can mitigate eutrophication in most large lakes but a dual N and P reduction may be needed in eutrophic lakes, especially in shallow ones (or bays). Our analysis helps clarify the long debate over whether N, P, or both control primary production. While these results imply that more resources be invested in nitrogen management, given the high costs of nitrogen pollution reduction, more comprehensive results from carefully designed experiments at different scales are needed to further verify this modification of the existing eutrophication mitigation paradigm.

High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery: An Assessment for the Southeastern Tibetan Plateau.

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.

Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes.

Biomass distribution among size classes follows a power law where the Log-abundance of taxa scales to Log-size with a slope that responds to environmental abiotic and biotic conditions. The interactions between ecological mechanisms controlling the slope of locally realized size-abundance relationships (SAR) are however not well understood. Here we tested how warming, nutrient levels, and grazing affect the slope of phytoplankton community SARs in decadal time-series from eight Swiss lakes of the peri-alpine region, which underwent environmental forcing due to climate change and oligotrophication. We expected rising temperature to have a negative effect on slope (favoring small phytoplankton), and increasing nutrient levels and grazing pressure to have a positive effect (benefiting large phytoplankton). Using a random forest approach to extract robust patterns from the noisy data, we found that the effects of temperature (direct and indirect through water column stability), nutrient availability (phosphorus and total biomass), and large herbivore (copepods and daphnids) grazing and selectivity on slope were non-linear and interactive. Increasing water temperature or total grazing pressure, and decreasing phosphorus levels, had a positive effect on slope (favoring large phytoplankton, which are predominantly mixotrophic in the lake dataset). Our results therefore showed patterns that were opposite to the expected long-term effects of temperature and nutrient levels, and support a paradigm in which (i) small phototrophic phytoplankton appear to be favored under high nutrients levels, low temperature and low grazing, and (ii) large mixotrophic algae are favored under oligotrophic conditions when temperature and grazing pressure are high. The effects of temperature were stronger under nutrient limitation, and the effects of nutrients and grazing were stronger at high temperature. Our study shows that the phytoplankton local SARs in lakes respond to both the independent and the interactive effects of resources, grazing and water temperature in a complex, unexpected way, and observations from long-term studies can deviate significantly from general theoretical expectations.

Ecoregional or site-specific lake nutrient criteria? Evidence from ecological fallacy

Abstract Ecoregional nutrient criteria are widely used but their validity has rarely been verified competed with site-specific criteria. In this study, we introduced ecological fallacy, which describes phenomenon that site-specific stressor-response relationships cannot be deducted from ecoregional relationship and vice versa, to explore the spatial scale of nutrient criteria. A long-term and nationwide water quality dataset of lakes and reservoirs was used to determine if ecological fallacy existed or not. Ecoregional and site-specific nutrient-Chlorophyll a relationships were built employing Bayesian linear model and hierarchical model, respectively. By comparing ecoregional and site-specific relationships, we found that ecological fallacy existed in each ecoregion. Ecoregional relationship may misidentify limiting nutrient or miscalculate the nutrient effect direction or magnitude. We found huge differences between estimated Chlorophyll a concentrations deduced from regional and site-specific relationships conditioning average TN or TP concentrations. Based on these results, we determined that lake nutrient criteria should be site-specific, primarily to avoid ecological fallacy rather than to improve their accuracy. These findings could guide the future nutrient criteria development. We further recommended partial pooling of data to develop stressor-response relationship facing with intensive environmental and ecological data.